Abstract
SUMMARYThe CUDA model for graphics processing units (GPUs) presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods and different data types. Identifying which options to use and when is a non‐trivial exercise. This paper explores the effect of these different options on the performance of a routine that evaluates sparse matrix–vector products (SpMV) across three different generations of NVIDIA GPU hardware. A process for analysing performance and selecting the subset of implementations that perform best is proposed. The potential for mapping sparse matrix attributes to optimal CUDA SpMV implementations is discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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